
"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
      from transformers import TvltImageProcessor

if is_speech_available():
      from transformers import TvltFeatureExtractor

from transformers import TvltProcessor



@require_torch
class 	__a	(   unittest.TestCase						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		"ZinengTang/tvlt-base"
              UpperCamelCase       			=		tempfile.mkdtemp()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   **UpperCAmelCase_			:				Optional[Any]      )->	List[Any]:







              """simple docstring"""
              return TvltImageProcessor.from_pretrained(self.checkpoint							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   **UpperCAmelCase_			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              return TvltFeatureExtractor.from_pretrained(self.checkpoint							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              shutil.rmtree(self.tmpdirname      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		self.get_image_processor()
              UpperCamelCase       			=		self.get_feature_extractor()

              UpperCamelCase       			=		TvltProcessor(image_processor=UpperCAmelCase_							,   feature_extractor=UpperCAmelCase_      )
              processor.save_pretrained(self.tmpdirname      )
              UpperCamelCase       			=		TvltProcessor.from_pretrained(self.tmpdirname      )

              self.assertIsInstance(processor.feature_extractor							,   UpperCAmelCase_      )
              self.assertIsInstance(processor.image_processor							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.get_image_processor()
              UpperCamelCase       			=		self.get_feature_extractor()

              UpperCamelCase       			=		TvltProcessor(image_processor=UpperCAmelCase_							,   feature_extractor=UpperCAmelCase_      )

              UpperCamelCase       			=		np.ones([12_000]      )

              UpperCamelCase       			=		feature_extractor(UpperCAmelCase_							,   return_tensors="np"      )
              UpperCamelCase       			=		processor(audio=UpperCAmelCase_							,   return_tensors="np"      )

              for key in audio_dict.keys():
                     self.assertAlmostEqual(audio_dict[key].sum()							,   input_processor[key].sum()							,   delta=1e-2      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		self.get_image_processor()
              UpperCamelCase       			=		self.get_feature_extractor()

              UpperCamelCase       			=		TvltProcessor(image_processor=UpperCAmelCase_							,   feature_extractor=UpperCAmelCase_      )

              UpperCamelCase       			=		np.ones([3, 224, 224]      )

              UpperCamelCase       			=		image_processor(UpperCAmelCase_							,   return_tensors="np"      )
              UpperCamelCase       			=		processor(images=UpperCAmelCase_							,   return_tensors="np"      )

              for key in image_dict.keys():
                     self.assertAlmostEqual(image_dict[key].sum()							,   input_processor[key].sum()							,   delta=1e-2      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		self.get_image_processor()
              UpperCamelCase       			=		self.get_feature_extractor()

              UpperCamelCase       			=		TvltProcessor(image_processor=UpperCAmelCase_							,   feature_extractor=UpperCAmelCase_      )

              UpperCamelCase       			=		np.ones([12_000]      )
              UpperCamelCase       			=		np.ones([3, 224, 224]      )

              UpperCamelCase       			=		processor(audio=UpperCAmelCase_							,   images=UpperCAmelCase_      )

              self.assertListEqual(list(inputs.keys()      )							,   ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]      )

              # test if it raises when no input is passed
              with pytest.raises(UpperCAmelCase_      ):
                     processor()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		self.get_image_processor()
              UpperCamelCase       			=		self.get_feature_extractor()

              UpperCamelCase       			=		TvltProcessor(image_processor=UpperCAmelCase_							,   feature_extractor=UpperCAmelCase_      )

              self.assertListEqual(
                  processor.model_input_names							,   image_processor.model_input_names + feature_extractor.model_input_names							,   msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"							,   )







"""simple docstring"""


class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		name
              UpperCamelCase       			=		value
              UpperCamelCase       			=		weight






       def __repr__(	self			:				int      )->	Any:







              """simple docstring"""
              return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              return self.value






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	int:







              """simple docstring"""
              return self.name






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[Any]:







              """simple docstring"""
              return self.weight






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Union[str, Any]:







              """simple docstring"""
              return self.value / self.weight





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		[]
       for i in range(len(UpperCAmelCase_					)					):
              menu.append(Things(name[i]		,   value[i]		,   weight[i]					)					)
       return menu



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		sorted(UpperCAmelCase_		,   key=UpperCAmelCase_		,   reverse=UpperCAmelCase_					)
       UpperCamelCase       			=		[]
       UpperCamelCase			,							UpperCamelCase       			=		0.0, 0.0
       for i in range(len(UpperCAmelCase_					)					):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]					)
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)



def 						lowerCamelCase__      (				)->				Dict:







       """simple docstring"""


if __name__ == "__main__":
      import doctest

      doctest.testmod()





